About The Position

Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. This opportunity involves designing original computational physics problems that simulate real physics research workflows, requiring Python programming (using Numpy, SciPy, Sympy) to solve. Problems must be computationally intensive, unsolvable manually within reasonable timeframes, and require non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics. Problems should be based on real research challenges or practical applications from physics practice. Solutions need to be verified using Python with standard physics simulation libraries, and problem statements must be clearly documented with verified correct answers.

Requirements

  • Degree in Physics (Theoretical, Experimental, or Computational) or related fields
  • Python proficiency for numerical validation. MATLAB, R, C, SQL, Numpy, Pandas, SciPy, domain-specific libraries, Stata or knowledge of any programming language can be equivalent
  • 2+ years of professional experience: applied, research, or teaching experience is applicable
  • Experience with numerical simulation methods
  • Ability to design problems that mirror real physics research workflows
  • Creative thinking in problem design across diverse physics areas
  • Familiarity with physics modeling and approximation techniques
  • Strong written English (C1+)

Responsibilities

  • Design original computational physics problems that simulate real physics research workflows
  • Create problems requiring Python programming to solve (using Numpy, SciPy, Sympy)
  • Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks)
  • Develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics
  • Base problems on real research challenges or practical applications from physics practice
  • Verify solutions using Python with standard physics simulation libraries
  • Document problem statements clearly and provide verified correct answers
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